Context Matters: Adaptive Mutation for Grammars
원문 초록
This work proposes Adaptive Facilitated Mutation, a self-adaptive mutation method for Structured Grammatical Evolution (SGE), biologically inspired by the theory of facilitated variation. In SGE, the genotype of individuals contains a list for each non-terminal of the grammar that defines the search space. In our proposed mutation, each individual contains an array with a different, self-adaptive mutation rate for each non-terminal. We also propose Function Grouped Grammars, a grammar design procedure, to enhance the benefits of the proposed mutation. Experiments were conducted on three symbolic regression benchmarks using Probabilistic Structured Grammatical Evolution (PSGE), a variant of SGE. Results show our approach is similar or better when compared with the standard grammar and mutation.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Discovering the underlying analytic structure within Standard Model constants using artificial intelligence
Structured Kolmogorov-Arnold Neural ODEs for Interpretable Learning and Symbolic Discovery of Nonlinear Dynamics
syren-baryon: Analytic emulators for the impact of baryons on the matter power spectrum
Improving Memory Efficiency for Training KANs via Meta Learning
Sparse Interpretable Deep Learning with LIES Networks for Symbolic Regression
Automated Modeling Method for Pathloss Model Discovery
토론
이 논문과 연결된 포럼 글이 아직 없습니다.